Managed Data Services
Ensure intelligent data operations with tech-enabled managed services — round-the-clock support for the IT systems that power your data lifecycle.
One size does not fit all
The need to effectively manage high-quality data for operational, commercial and regulatory requirements is growing. Traditional ITIL support and infrastructure services aren't equipped to meet these demands.
Inadequate, complex in-house data management systems and a lack of specialist skills make it difficult to operate, monitor and continuously improve modern data ecosystems — and that gap shows up as compliance risk, slow incident response and stalled value delivery.
AI/ML-led managed services backed by cloud-hosted tools
Rapinno brings together a full suite of Managed Data Services to support the end-to-end data lifecycle. We help enterprises manage their data and analytics ecosystems using service catalogues, managed curation, ingestion, pipelines, backup and archival.
We also support AI solutions and bots in production, execute enhancement projects, and ensure data access, user management, security and availability — combining DataOps, MLOps, cloud data management and SRE skills under one accountable partner.
Our managed data offerings
End-to-end DataOps and MLOps capabilities that keep your data ecosystem running, evolving and creating value.
DataOps
Bring distributed analytical, storage, processing and visualization workloads under a common pipelined workflow — automated for fast, repeatable deployment.
MLOps
CI/CD and retraining pipelines for AI applications — automating workspace, compute, ML pipelines and scoring images across environments using AutoML.
CI/CD, Scheduling & Monitoring
Production-grade scheduling, governance and monitoring across legacy and cloud systems — keeping pipelines reliable and observable.
Cloud Data Management
Day-2 operations across AWS, Azure (including Fabric), GCP, Databricks and Snowflake — covering performance, cost optimization and capacity.
Site Reliability Engineering
SRE practices applied to data platforms — SLOs, error budgets, incident response and continuous reliability improvement.
Data Curation, Ingestion & Pipelines
Managed services for ingestion, curation, transformation and quality — keeping the data layer fresh and trustworthy for downstream consumers.
Backup, Archival & Recovery
Backup, archival and recovery operations aligned to RPO/RTO targets — protecting data assets and supporting compliance audits.
Bot & AI Solution Maintenance
Production support and enhancement for bots, ML models and AI workflows — including drift monitoring, retraining and lifecycle management.
Scalable, resilient, predictable data operations
Increased operational efficiency
Reduce time-to-market, effort and complexity in standing up and managing modern data infrastructure and quality programs.
Reduced compliance risk
Identify what data the business has, where it's stored and who has access — keeping critical data governed, accurate and current.
Increased innovation
Free internal teams from operational toil so they can focus on higher-value, business-defining initiatives.
AI/ML-led operations
Smart automation and ML-based anomaly detection mean issues are surfaced and often resolved before they impact the business.
Common questions
What does a typical managed data services engagement cover?+
Operations of pipelines and platforms, monitoring and incident response, data quality SLAs, governance enforcement, ML model maintenance, cost optimization and continuous enhancement — all under defined SLOs.
Do you operate 24x7?+
Yes. We offer 24x7, follow-the-sun support models tuned to your business hours and criticality tiers — with on-call rotations and clear escalation paths.
Which commercial models do you offer?+
Fixed bid, time-and-materials and outcome-based managed services — typically structured around platform tier, ticket volume and SLA targets.
Can you take over an existing platform?+
Yes. We run a structured transition: discovery, knowledge transfer, runbook hardening and a steady-state cutover — typically completed in 6–12 weeks.
Ready to offload data operations to specialists?
Let's design a managed services model that keeps your data platform reliable, compliant and continuously improving.